Oncogenicity Variant Interpreter (OncoVI) Supports Harmonized Somatic Variant Interpretation in Precision Oncology

Read full text
Authors: Maria Giulia Carta, Lars Tögel, Annett Hölsken, Christoph Schubart, Heinrich Sticht, Robert Stoehr, Silvia Spoerl, Norbert Meidenbauer, Arndt Hartmann, Paolo Magni, Florian Haller, Fulvia Ferrazzi
Cited by: 2
References: 39

Abstract

Accurate and reproducible interpretation of somatic variants is fundamental for therapy decision-making in cancer patients. To harmonise and automate oncogenicity classification, Oncogenicity Variant Interpreter (OncoVI), an open-source, Python-based implementation of the ClinGen/CGC/VICC oncogenicity guidelines was developed. For each of the guideline criteria, the textual descriptions were interpreted and publicly available resources identified to be used as reference. Starting from the genomic coordinates of a variant, OncoVI automatically performs functional annotation, collects relevant evidence from the integrated resources, evaluates each criterion, and provides a final oncogenicity classification. OncoVI achieved an accuracy of 80% on a gold-standard set of 93 somatic variants provided by the guidelines, with a sensitivity of 88% for Oncogenic/Likely Oncogenic variants. When applied to a real-world set of 7,802 variants from 557 participants previously evaluated by the Molecular Tumour Board (MTB) Erlangen, OncoVI showed 79% concordance with the prior MTB assessment of variant impact on protein function. In addition, expert reassessment of 135 MTB variants, conducted in accordance with the oncogenicity guidelines, further confirmed both the validity of OncoVI implementation and the appropriateness of the identified resources. Taken together, OncoVI provides significant support for the harmonised and reproducible oncogenicity classification of somatic variants across institutions.

Fields of Study & Subjects

Cancer Genomics and Diagnostics PARP inhibition in cancer therapy Genetic factors in colorectal cancer

Publication details

Journal / source
Journal of Molecular Diagnostics
Publisher
Elsevier BV
Volume
28
Issue
6
Pages
469–484
Publication date
3 April 2026
Language
EN